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DVM-SLC: A Dual-View Meta-Aware Model for Reliable Multi-Class Skin Lesion Classification from Clinical and Dermoscopic Images. [PDF]
Diagnostics (Basel)Çetinel G, Gül S, Öztaş Kara R.
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Explainability in context: calibrating appropriate trust and reliance in artificial intelligence. [PDF]
J Am Med Inform AssocDavis SE, Salwei ME.
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2020
An aspect of User friendly AI involves explanation and better transparency of AI. Explainable AI(XAI) is an emerging area of research dedicated to explain and elucidate AI systems. In order to accomplish such an explanation, XAI uses a variety of tools, devices and frameworks.
Swaroop Panda, Shatarupa Thakurta Roy
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An aspect of User friendly AI involves explanation and better transparency of AI. Explainable AI(XAI) is an emerging area of research dedicated to explain and elucidate AI systems. In order to accomplish such an explanation, XAI uses a variety of tools, devices and frameworks.
Swaroop Panda, Shatarupa Thakurta Roy
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993
'Explaining away' is a common pattern of reasoning in which the confirmation of one cause of an observed or believed event reduces the need to invoke alternative causes. The opposite of explaining away also an occur, where the confirmation of one cause increases belief in another. A general qualitative probabilistic analysis of intercausal reasoning is
Michael P. Wellman, Max Henrion
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'Explaining away' is a common pattern of reasoning in which the confirmation of one cause of an observed or believed event reduces the need to invoke alternative causes. The opposite of explaining away also an occur, where the confirmation of one cause increases belief in another. A general qualitative probabilistic analysis of intercausal reasoning is
Michael P. Wellman, Max Henrion
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XRDS: Crossroads, The ACM Magazine for Students, 2019
How good are you at explaining your decisions? Are you better than a machine? Today, AI systems are being asked to explain their decisions. This article explores the challenges in solving this problem and approaches researchers are pursuing.
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How good are you at explaining your decisions? Are you better than a machine? Today, AI systems are being asked to explain their decisions. This article explores the challenges in solving this problem and approaches researchers are pursuing.
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IEEE Multimedia, 1995
The upcoming MHEC standard, under development by ISO's Multimedia Hypermedia Experts Group, defines a system-independent encoding of the structure information used for storing, exchanging, and executing multimedia presentations. This article describes the building blocks of an MHEG-encoded presentation through a running example. Our experience involved
Meyer-Boudnik, Thomas +1 more
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The upcoming MHEC standard, under development by ISO's Multimedia Hypermedia Experts Group, defines a system-independent encoding of the structure information used for storing, exchanging, and executing multimedia presentations. This article describes the building blocks of an MHEG-encoded presentation through a running example. Our experience involved
Meyer-Boudnik, Thomas +1 more
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Explaining the actions of the explainers
Erkenntnis, 1985Explaining has been seen by many as the paradigm display of rationality on our part, and rationality as our most distinctive attribute. To be an explainer is, along the way, to be one who can generalize, infer, predict, offer justifications, conform to the rules of evidence, and, in order to offer more ambitious explanations, one must be a theorizer ...
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Explaining Explanation For “Explainable Ai”
Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2018What makes for an explanation of “black box” AI systems such as Deep Nets? We reviewed the pertinent literatures on explanation and derived key ideas. This set the stage for our empirical inquiries, which include conceptual cognitive modeling, the analysis of a corpus of cases of "naturalistic explanation" of computational systems, computational ...
Robert R. Hoffman +2 more
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Quarterly Journal of Experimental Psychology, 2011
How do reasoners deal with inconsistencies? James (1907) believed that the rational solution is to revise your beliefs and to do so in a minimal way. We propose an alternative: You explain the origins of an inconsistency, which has the side effect of a revision to your beliefs.
Sangeet S, Khemlani +1 more
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How do reasoners deal with inconsistencies? James (1907) believed that the rational solution is to revise your beliefs and to do so in a minimal way. We propose an alternative: You explain the origins of an inconsistency, which has the side effect of a revision to your beliefs.
Sangeet S, Khemlani +1 more
openaire +2 more sources